This documentation is for scikit-learn version 0.11-gitOther versions


If you use the software, please consider citing scikit-learn.

This page


This project is a community effort, and everyone is welcome to contribute.

The project is hosted on

Submitting a bug report

In case you experience issues using the package, do not hesitate to submit a ticket to the Bug Tracker.

You are also welcome to post there feature requests or links to pull-requests.

Retrieving the latest code

We use Git for version control and GitHub for hosting our main repository.

You can check the latest sources with the command:

git clone git://

or if you have write privileges:

git clone

If you run the development version, it is cumbersome to re-install the package each time you update the sources. It is thus preferred that you add the scikit-directory to your PYTHONPATH and build the extension in place:

python build_ext --inplace

On Unix you can simply type make in the top-level folder to build in-place and launch all the tests. Have a look at the Makefile for additional utilities.

Contributing code


To avoid duplicated work it is highly advised to contact the developers mailing list before starting work on a non-trivial feature.

How to contribute

The prefered way to contribute to Scikit-Learn is to fork the main repository on GitHub:

  1. Create an account on GitHub if you don’t have one already.

  2. Fork the project repository: click on the ‘Fork’ button, at the top, center of the page. This creates a copy of the code on the GitHub server where you can work.

  3. Clone this copy to your local disk:

    $ git clone
  4. Work on this copy, on your computer, using git to do the version control:

    $ git add modified_files
    $ git commit
    $ git push origin master

    and so on.

If your changes are not just trivial fixes, it is better to directly work in a branch with the name of the feature your are working on. In this case, replace step 4 by step 5:

  1. Create a branch to host your changes and publish it on your public repo:

    $ git checkout -b my-feature
    $ git add modified_files
    $ git commit
    $ git push origin my-feature

When you are ready, and you have pushed your changes on your github repo, go the web page of the repo, and click on ‘Pull request’ to send us a pull request. This will send an email to the commiters, but might also send an email to the mailing list in order to get more visibility.


In the above setup, your origin remote-repository points to YourLogin/scikit-learn.git. If you wish to fetch/merge from the main repository instead of your forked one, you’ll need to add another remote to use instead of origin. If we choose the name upstream for it, the command will be:

$ git remote add upstream

(If any of the above seems like magic to you, then look up the Git documentation on the web.)

It is recommented to check that your contribution complies with the following rules before submitting a pull request:

  • Follow the coding-guidelines (see below).

  • When applicable, use the Validation tools and other code in the sklearn.utils submodule. A list of utility routines available for developers can be found in the Utilities for Developers page.

  • All public methods should have informative docstrings with sample usage presented as doctests when appropriate.

  • All other tests pass when everything is rebuilt from scrath, under Unix, check with (from the toplevel source folder):

    $ make
  • At least one example script in the examples/ folder. Have a look at other examples for reference. Example should demonstrate why this method is useful in practice and if possible compare it to other methods available in the scikit.

  • At least one paragraph of narrative documentation with links to references in the literature (with PDF links when possible) and the example.

    The documentation should also include expected time and space complexity of the algorithm and scalablity, e.g. “this algorithm can scale to a large number of samples > 100000, but does not scale in dimensionality: n_features is expected to be lower than 100”.

    To build the documentation see documentation below.

You can also check for common programming errors with the following tools:

  • Code with a good unittest coverage (at least 80%), check with:

    $ pip install nose coverage
    $ nosetests --with-coverage path/to/tests_for_package
  • No pyflakes warnings, check with:

    $ pip install pyflakes
    $ pyflakes path/to/
  • No PEP8 warnings, check with:

    $ pip install pep8
    $ pep8 path/to/

Bonus points for contributions that include a performance analysis with a benchmark script and profiling output (please report on the mailing list or on the github wiki).

Also check out the following guide on How to optimize for speed for more details on profiling and cython optimizations.


The current state of the scikit-learn code base is not compliant with all of those guidelines but we expect that enforcing those constraints on all new contributions will get the overall code base quality in the right direction.

EasyFix Issues

The best way to get your feet wet is to pick up an issue from the issue tracker that are labeled as EasyFix. This means that the knowledge needed to solve the issue is low, but still you are helping the project and letting more experienced developers concentrate on other issues.


We are glad to accept any sort of documentation: function docstrings, rst docs (like this one), tutorials, etc. Rst docs live in the source code repository, under directory doc/.

You can edit them using any text editor and generate the html docs by typing from the doc/ directory make html (or make html-noplot, see README in that directory for more info). That should create a directory _build/html/ with html files that are viewable in a web browser.

For building the documentation, you will need sphinx and matplotlib.

When you are writing documentation, it is important to keep a good compromise between mathematical and algorithmic details, and giving intuitions to the reader on what the algorithm does. It is best to always start with a small paragraph with a hand waiving explanation of what the method does to the data and a figure (coming from an example) ilustrating it.


Sphinx version

While we do our best to have the documentation build under as many version of Sphinx as possible, the different versions tend to behave slightly differently. To get the best results, you should use version 1.0.

Developers web site

More information can be found at the developer’s wiki.

Other ways to contribute

Code is not the only way to contribute to this project. For instance, documentation is also a very important part of the project and ofter doesn’t get as much attention as it deserves. If you find a typo in the documentation, or have made improvements, don’t hesitate to send an email to the mailing list or a github pull request. Full documentation can be found under directory doc/.

It also helps us if you spread the word: reference it from your blog, articles, link to us from your website, or simply by saying “I use it”:

Coding guidelines

The following are some guidelines on how new code should be written. Of course, there are special cases and there will be exceptions to these rules. However, following these rules when submitting new code makes the review easier so new code can be integrated in less time.

Uniformly formatted code makes it easier to share code ownership. The scikit learn tries to follow closely the official Python guidelines detailed in PEP8 that details how code should be formatted, and indented. Please read it and follow it.

In addition, we add the following guidelines:

  • Use underscores to separate words in non class names: n_samples rather than nsamples.
  • Avoid multiple statements on one line. Prefer a line return after a control flow statement (if/for).
  • Use relative imports for references inside scikit-learn.
  • Please don’t use `import *` in any case. It is considered harmful by the official Python recommendations. It makes the code harder to read as the origin of symbols is no longer explicitly referenced, but most important, it prevents using a static analysis tool like pyflakes to automatically find bugs in scikit.
  • Use the numpy docstring standard in all your docstrings.

A good example of code that we like can be found here.

Input validation

The module sklearn.utils contains various functions for doing input validation/conversion. Sometimes, np.asarray suffices for validation; do not use np.asanyarray or np.atleast_2d, since those let NumPy’s np.matrix through, which has a different API (e.g., * means dot product on np.matrix, but Hadamard product on np.ndarray).

In other cases, be sure to call safe_asarray, atleast2d_or_csr, as_float_array or array2d on any array-like argument passed to a scikit-learn API function. The exact function to use depends mainly on whether scipy.sparse matrices must be accepted.

For more information, refer to the Utilities for Developers page.

Random Numbers

If your code depends on a random number generator, do not use numpy.random.random() or similar routines. To ensure repeatability in error checking, the routine should accept a keyword random_state and use this to construct a numpy.random.RandomState object. See sklearn.utils.check_random_state in Utilities for Developers.

Here’s a simple example of code using some of the above guidelines:

from sklearn.utils import array2d, check_random_state

def choose_random_sample(X, random_state=0):
    Choose a random point from X

    X : array-like, shape = (n_samples, n_features)
        array representing the data
    random_state : RandomState or an int seed (0 by default)
        A random number generator instance to define the state of the
        random permutations generator.

    x : numpy array, shape = (n_features,)
        A random point selected from X
    X = array2d(X)
    random_state = check_random_state(random_state)
    i = random_state.randint(X.shape[0])
    return X[i]

APIs of scikit-learn objects

To have a uniform API, we try to have a common basic API for all the objects. In addition, to avoid the proliferation of framework code, we try to adopt simple conventions and limit to a minimum the number of methods an object has to implement.

Different objects

The main objects of the scikit learn are (one class can implement multiple interfaces):


The base object, implements:

estimator =

For supervised learning, or some unsupervised problems, implements:

prediction = obj.predict(data)

For filtering or modifying the data, in a supervised or unsupervised way, implements:

new_data = obj.transform(data)

When fitting and transforming can be performed much more efficiently together than separately, implements:

new_data = obj.fit_transform(data)

A model that can give a goodness of fit or a likelihood of unseen data, implements (higher is better):

score = obj.score(data)


The API has one predominant object: the estimator. A estimator is an object that fits a model based on some training data and is capable of inferring some properties on new data. It can be for instance a classifier or a regressor. All estimators implement the fit method:, y)

All built-in estimators also have a set_params method, which sets data-independent parameters (overriding previous parameter values passed to __init__). This method is not required for an object to be an estimator.

All estimators should inherit from sklearn.base.BaseEstimator.


This concerns the object creation. The object’s __init__ method might accept as arguments constants that determine the estimator behavior (like the C constant in SVMs).

It should not, however, take the actual training data as argument, as this is left to the fit() method:

clf2 = SVC(C=2.3)
clf3 = SVC([[1, 2], [2, 3]], [-1, 1]) # WRONG!

The arguments that go in the __init__ should all be keyword arguments with a default value. In other words, a user should be able to instanciate an estimator without passing to it any arguments.

The arguments in given at instanciation of an estimator should all correspond to hyper parameters describing the model or the optimisation problem that estimator tries to solve.

In addition, every keyword argument given to the ``__init__`` should correspond to an attribute on the instance. The scikit relies on this to find what are the relevent attributes to set on an estimator when doing model selection.

To summarize, a __init__ should look like:

def __init__(self, param1=1, param2=2):
    self.param1 = param1
    self.param2 = param2

There should be no logic, and the parameters should not be changed. The corresponding logic should be put when the parameters are used. The following is wrong:

def __init__(self, param1=1, param2=2, param3=3):
    # WRONG: parameters should not be modified
    if param1 > 1:
        param2 += 1
    self.param1 = param1
    # WRONG: the object's attributes should have exactly the name of
    # the argument in the constructor
    self.param3 = param2

Scikit-Learn relies on this mechanism to introspect object to set their parameters by cross-validation.


The next thing you’ll probably want to do is to estimate some parameters in the model. This is implemented in the .fit() method.

The fit method takes as argument the training data, which can be one array in the case of unsupervised learning, or two arrays in the case of supervised learning.

Note that the model is fitted using X and y but the object holds no reference to X, y. There are however some exceptions to this, as in the case of precomputed kernels where you need to store access these data in the predict method.

X array-like, with shape = [N, D], where N is the number of samples and D is the number of features.
y array, with shape = [N], where N is the number of samples.
kwargs optional data dependent parameters.

X.shape[0] should be the same as y.shape[0]. If this requisite is not met, an exception of type ValueError should be raised.

y might be ignored in the case of unsupervised learning. However to make it possible to use the estimator as part of a pipeline that can mix both supervised and unsupervised transformers even unsupervised estimators are kindly ask to accept a y=None keyword argument in the second position that is just ignored by the estimator.

The method should return the object (self). This pattern is useful to be able to implement quick one liners in an ipython session such as:

y_predicted = SVC(C=100).fit(X_train, y_train).predict(X_test)

Depending on the nature of the algorithm fit can sometimes also accept additional keywords arguments. However any parameter that can have a value assigned prior having access to the data should be an __init__ keyword argument. fit parameters should be restricted to directly data dependent variables. For instance a Gram matrix or an affinity matrix which are precomputed from the data matrix X are data dependent. A tolerance stopping criterion tol is not directly data dependent (although the optimal value according to some scoring function probably is).

Any attribute that ends with _ is expected to be overridden when you call fit a second time without taking any previous value into account: fit should be idempotent.

Optional Arguments

In iterative algorithms, number of iterations should be specified by an int called n_iter.

Unresolved API issues

Some things are must still be decided:

  • what should happen when predict is called before than fit() ?
  • which exception should be raised when arrays’ shape do not match in fit() ?

Working notes

For unresolved issues, TODOs, remarks on ongoing work, developers are adviced to maintain notes on the github wiki:

Specific models

In linear models, coefficients are stored in an array called coef_, and independent term is stored in intercept_.